Introduction to kardl

Huseyin karamelikli and Huseyin Utku Demir

2026-06-24

Introduction

The kardl package is an R tool for estimating symmetric and asymmetric Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) models, designed for econometricians and researchers analyzing cointegration and dynamic relationships in time series data. It offers flexible model specifications, allowing users to include deterministic variables, asymmetric effects for short- and long-run dynamics, and trend components. The package supports customizable lag structures, model selection criteria (AIC, BIC, AICc, HQ), and parallel processing for computational efficiency. Key features include:

This vignette demonstrates how to use the kardl() function to estimate an asymmetric ARDL model, perform diagnostic tests, and visualize results, using economic data from Turkey.

Installation

kardl in R can easily be installed from its CRAN repository:

install.packages("kardl")
library(kardl)

Alternatively, you can use the devtools package to load directly from GitHub:

# Install required packages
install.packages(c(
  "stats", "msm", "lmtest", "nlWaldTest", "car", "strucchange",
  "utils", "ggplot2"
))
# Install kardl from GitHub
install.packages("devtools")
devtools::install_github("karamelikli/kardl")

Load the package:

library(kardl)

Unique Features and Methodological Contributions

The kardl package implements several methodological extensions and improvements for ARDL/NARDL modelling that go beyond standard implementations available in R and other software:

These features make kardl particularly suitable for researchers needing fine-grained control over asymmetric dynamics and small-sample inference.

Estimating an asymmetric ARDL Model

This example estimates an asymmetric ARDL model to analyze the dynamics of exchange rate pass-through to domestic prices in Turkey, using a sample dataset (imf_example_data) with variables for Consumer Price Index (CPI), Exchange Rate (ER), Producer Price Index (PPI), and a COVID-19 dummy variable.

Step 1: Data Preparation

Assume imf_example_data contains monthly data for CPI, ER, PPI, and a COVID dummy variable. We prepare the data by ensuring proper formatting and adding the dummy variable. We retrieve data from the IMF’s International Financial Statistics (IFS) dataset and prepare it for analysis.

Note: The imf_example_data is a placeholder for demonstration purposes. You should replace it with your actual dataset. The data can be loaded by readxl or other data import functions.

Step 2: Define the Model Formula

We define the model formula using R’s formula syntax, incorporating asymmetric effects and deterministic variables. We use asymmetric() for variables with both short- and long-run asymmetry, lasymmetric() for long-run asymmetry, sasymmetric() for short-run asymmetry, and deterministic() for fixed dummy variables. The trend term includes a linear time trend in the model.

# Define the model formula
my_formula <- CPI ~ ER + PPI + asymmetric(ER + PPI) + deterministic(covid) +
  trend

Indeed, the formula syntax is flexible, allowing for various combinations of asymmetric and deterministic variables. The following variations of the formula are equivalent and will yield the same model specification:

same_formula <- y ~ asymmetric(x1) +
  sasymmetric(x2 + x3) +
  lasymmetric(x4 + x5) +
  deterministic(dummy1) + trend
same_formula <- y ~ asymmetric(x1) +
  sasymmetric(x2 + x3) +
  lasymmetric(x4 + x5) +
  deterministic(dummy1) + trend
same_formula <- y ~ asym(x1) + sasym(x2 + x3) + lasym(x4 + x5) +
  det(dummy1) + trend
same_formula <- y ~ a(x1) + s(x2 + x3) + l(x4 + x5) + d(dummy1) + trend

Step 3: Model Estimation

We estimate the ARDL model using different mode settings to demonstrate flexibility in lag selection. The kardl() function supports various modes: "grid", "grid_custom", "quick", or a user-defined lag vector.

Using mode = "grid"

The "grid" mode evaluates all lag combinations up to maxlag and provides console feedback.

# Set model options
kardl_set(criterion = "BIC", different_asym_lag = TRUE, data = imf_example_data)
# Estimate model with grid mode
kardl_model <- kardl(
  data = imf_example_data, formula = my_formula,
  maxlag = 4, mode = "grid"
)
# View results
kardl_model
## Optimal lags for each variable ( BIC ):
## CPI: 1, ER_POS: 1, ER_NEG: 0, PPI_POS: 3, PPI_NEG: 0 
## 
## Call:
## lm(formula = my_formula, data = model_data)
## 
## Coefficients:
##  (Intercept)        L1.CPI     L1.ER_POS     L1.ER_NEG    L1.PPI_POS  
##   -0.0662799    -0.0167504     0.0138795     0.0346587     0.0420485  
##   L1.PPI_NEG      L1.d.CPI   L0.d.ER_POS   L0.d.ER_NEG  L0.d.PPI_POS  
##    0.0396081     0.3969313     0.1242241    -0.0229369     0.0420020  
## L0.d.PPI_NEG         covid         trend  
##   -0.0039507     0.0036707    -0.0000213

Summary of the model provides detailed information about the estimated coefficients, standard errors, t-values, and significance levels.

# Display model summary
summary(kardl_model)
## 
## Call:
## lm(formula = my_formula, data = model_data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.044033 -0.009369 -0.001300  0.007786  0.113556 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.0662799  0.0233710  -2.836 0.004772 ** 
## L1.CPI       -0.0167504  0.0048668  -3.442 0.000631 ***
## L1.ER_POS     0.0138795  0.0052938   2.622 0.009039 ** 
## L1.ER_NEG     0.0346587  0.0081800   4.237 2.74e-05 ***
## L1.PPI_POS    0.0420485  0.0096352   4.364 1.58e-05 ***
## L1.PPI_NEG    0.0396081  0.0110457   3.586 0.000372 ***
## L1.d.CPI      0.3969313  0.0399609   9.933  < 2e-16 ***
## L0.d.ER_POS   0.1242241  0.0185934   6.681 6.94e-11 ***
## L0.d.ER_NEG  -0.0229369  0.0495520  -0.463 0.643668    
## L0.d.PPI_POS  0.0420020  0.0166326   2.525 0.011899 *  
## L0.d.PPI_NEG -0.0039507  0.0139364  -0.283 0.776936    
## covid         0.0036707  0.0053298   0.689 0.491354    
## trend        -0.0000213  0.0002533  -0.084 0.933010    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0156 on 455 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.6099, Adjusted R-squared:  0.5996 
## F-statistic: 59.28 on 12 and 455 DF,  p-value: < 2.2e-16

Using User-Defined Lags

Specify custom lags to bypass automatic lag selection:

kardl_model2 <- kardl(
  data = imf_example_data, my_formula,
  mode = c(2, 1, 1, 3, 0)
)
# View results
kardl_extract(kardl_model2, "opt_lag")
##     CPI  ER_POS  ER_NEG PPI_POS PPI_NEG 
##       2       1       1       3       0
# Display model summary
summary(kardl_model2)
## 
## Call:
## lm(formula = my_formula, data = model_data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.054516 -0.008329 -0.001178  0.006787  0.104278 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.0382834  0.0226694  -1.689 0.091962 .  
## L1.CPI       -0.0123942  0.0047187  -2.627 0.008921 ** 
## L1.ER_POS     0.0110505  0.0051609   2.141 0.032798 *  
## L1.ER_NEG     0.0268623  0.0080054   3.356 0.000860 ***
## L1.PPI_POS    0.0532642  0.0096979   5.492 6.67e-08 ***
## L1.PPI_NEG    0.0452663  0.0107751   4.201 3.21e-05 ***
## L1.d.CPI      0.3750078  0.0444934   8.428 4.88e-16 ***
## L2.d.CPI     -0.0865443  0.0423947  -2.041 0.041800 *  
## L0.d.ER_POS   0.1119077  0.0179998   6.217 1.16e-09 ***
## L1.d.ER_POS   0.0889105  0.0189930   4.681 3.79e-06 ***
## L0.d.ER_NEG  -0.0051813  0.0476798  -0.109 0.913515    
## L1.d.ER_NEG   0.0047909  0.0475913   0.101 0.919860    
## L0.d.PPI_POS  0.0487238  0.0160447   3.037 0.002532 ** 
## L1.d.PPI_POS -0.0010288  0.0144643  -0.071 0.943329    
## L2.d.PPI_POS -0.0544024  0.0143719  -3.785 0.000174 ***
## L3.d.PPI_POS -0.0499891  0.0137240  -3.642 0.000302 ***
## L0.d.PPI_NEG  0.0056668  0.0134844   0.420 0.674505    
## covid         0.0031412  0.0050898   0.617 0.537448    
## trend        -0.0003309  0.0002472  -1.338 0.181531    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.01479 on 446 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.655,  Adjusted R-squared:  0.641 
## F-statistic: 47.03 on 18 and 446 DF,  p-value: < 2.2e-16

Using All Variables

Use the . operator to include all variables except the dependent variable:

kardl_set(data = imf_example_data)
kardl(formula = CPI ~ . + deterministic(covid), mode = "grid")
## Optimal lags for each variable ( BIC ):
## CPI: 1, ER: 1, PPI: 1 
## 
## Call:
## lm(formula = my_formula, data = model_data)
## 
## Coefficients:
## (Intercept)       L1.CPI        L1.ER       L1.PPI     L1.d.CPI      L0.d.ER  
##    0.075004    -0.019754     0.020240     0.001600     0.489064     0.113932  
##    L0.d.PPI        covid  
##   -0.001353    -0.002127

Visualizing Lag Criteria

The lag_criteria component contains lag combinations and their criterion values. We visualize these to compare model selection criteria (AIC, BIC, HQ).

library(dplyr)
library(tidyr)
library(ggplot2)
# Convert lag_criteria to a data frame
lag_criteria <- as.data.frame(kardl_extract(kardl_model, "lag_criteria"))
colnames(lag_criteria) <- c("lag", "AIC", "BIC", "AICc", "HQ")
lag_criteria <- lag_criteria |> mutate(across(c(AIC, BIC, HQ), as.numeric))

# Pivot to long format
lag_criteria_long <- lag_criteria |>
  select(-AICc) |>
  pivot_longer(
    cols = c(AIC, BIC, HQ),
    names_to = "Criteria",
    values_to = "Value"
  )

# Find minimum values
min_values <- lag_criteria_long |>
  group_by(Criteria) |>
  slice_min(order_by = Value) |>
  ungroup()

# Plot
ggplot(
  lag_criteria_long,
  aes(x = lag, y = Value, color = Criteria, group = Criteria)
) +
  geom_line() +
  geom_point(
    data = min_values, aes(x = lag, y = Value),
    color = "red", size = 3, shape = 8
  ) +
  geom_text(
    data = min_values, aes(x = lag, y = Value, label = lag),
    vjust = 1.5, color = "black", size = 3.5
  ) +
  labs(
    title = "Lag Criteria Comparison",
    x = "Lag Configuration",
    y = "Criteria Value"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Error Correction Model (ECM) Estimation

The ecm() function estimates a Restricted ECM for cointegration testing. We specify the same formula and lag structure as in the ARDL model.

ecm_model <- ecm(
  data = imf_example_data, formula = my_formula,
  maxlag = 4, mode = "grid_custom"
)
# View results
summary(ecm_model)
## 
## Call:
## lm(formula = shortrun_eq, data = ecm_data)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.069034 -0.008966 -0.000360  0.007383  0.098004 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.840e-02  2.767e-03   6.650 8.50e-11 ***
## EcmRes       -7.988e-03  4.168e-03  -1.916 0.055947 .  
## L1.d.CPI      4.566e-01  3.738e-02  12.217  < 2e-16 ***
## L0.d.ER_POS   1.237e-01  1.863e-02   6.643 8.86e-11 ***
## L1.d.ER_POS   9.888e-02  1.895e-02   5.218 2.76e-07 ***
## L0.d.ER_NEG   4.782e-02  4.861e-02   0.984 0.325705    
## L0.d.PPI_POS  1.974e-02  1.524e-02   1.296 0.195793    
## L1.d.PPI_POS  2.079e-02  1.461e-02   1.423 0.155302    
## L2.d.PPI_POS -3.840e-02  1.460e-02  -2.631 0.008815 ** 
## L3.d.PPI_POS -3.676e-02  1.403e-02  -2.620 0.009085 ** 
## L0.d.PPI_NEG -6.570e-04  1.387e-02  -0.047 0.962248    
## covid         1.182e-02  3.231e-03   3.657 0.000285 ***
## trend        -4.622e-05  7.953e-06  -5.812 1.17e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0156 on 452 degrees of freedom
## Multiple R-squared:  0.611,  Adjusted R-squared:  0.6007 
## F-statistic: 59.16 on 12 and 452 DF,  p-value: < 2.2e-16

Step 4: Long-Run Coefficients

We calculate long-run coefficients using kardl_longrun(), which standardizes coefficients by dividing them by the negative of the dependent variable’s long-run parameter.

# Long-run coefficients
my_long <- kardl_longrun(kardl_model)
my_long
## 
## Call:
## kardl_longrun.kardl_lm(kardl_model = kardl_model)
## 
## Coefficients:
##  L1.ER_POS   L1.ER_NEG  L1.PPI_POS  L1.PPI_NEG  
##     0.8286      2.0691      2.5103      2.3646

The summary() function provides detailed information about the long-run coefficients, including standard errors, t-values, and significance levels.

# Summary of long-run coefficients
summary(my_long)
## 
## Call:
## kardl_longrun.kardl_lm(kardl_model = kardl_model)
## 
## Estimation type:
## Long-run multipliers 
## 
## Coefficients:
##            Estimate Std. Error t value  Pr(>|t|)    
## L1.ER_POS   0.82861    0.15902  5.2107 2.858e-07 ***
## L1.ER_NEG   2.06913    0.41649  4.9680 9.593e-07 ***
## L1.PPI_POS  2.51030    0.85303  2.9428 0.0034187 ** 
## L1.PPI_NEG  2.36460    0.71337  3.3147 0.0009908 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Step 5: Symmetry Test

The symmetrytest() function performs Wald tests to assess short- and long-run asymmetry in the model.

ast <- imf_example_data |>
  kardl(
    CPI ~ ER + PPI + asymmetric(ER + PPI) +
      deterministic(covid) + trend,
    mode = c(1, 2, 3, 0, 1),
    data = _
  ) |>
  symmetrytest()
ast
## 
## KARDL Symmetry Test Results
## Symmetry Test Results - Long-run:
## =======================
##     Df  Sum of Sq    Mean Sq F value Pr(>F)
## ER   1 0.00049756 0.00049756  2.1649 0.1419
## PPI  1 0.00016439 0.00016439  0.7153 0.3982
## 
## Symmetry Test Results - Short-run:
## =======================
##     Df  Sum of Sq    Mean Sq F value Pr(>F)
## ER   1 0.00038607 0.00038607  1.6798 0.1956
## PPI  1 0.00017426 0.00017426  0.7582 0.3844

Summary of the symmetry test provides detailed results for both long-run and short-run asymmetry tests, including F-values, p-values, hypotheses, and test decisions.

# Summary of symmetry test
summary(ast)
## 
## Long-run symmetry tests
## -----------------------
##           F  Pr(>F)
## ER  2.16491 0.14190
## PPI 0.71527 0.39815
## 
## Hypotheses and decisions:
## 
## Variable: ER 
## H0: - Coef(L1.ER_POS)/Coef(L1.CPI) = - Coef(L1.ER_NEG)/Coef(L1.CPI) 
## H1: At least one coefficient differs from zero. 
## Decision: Fail to Reject H0 at 5% level. Indicating long-run symmetry for variable ER. 
## 
## Variable: PPI 
## H0: - Coef(L1.PPI_POS)/Coef(L1.CPI) = - Coef(L1.PPI_NEG)/Coef(L1.CPI) 
## H1: At least one coefficient differs from zero. 
## Decision: Fail to Reject H0 at 5% level. Indicating long-run symmetry for variable PPI. 
## 
## 
## Short-run symmetry tests
## ------------------------
##           F  Pr(>F)
## ER  1.67981 0.19562
## PPI 0.75820 0.38436
## 
## Hypotheses and decisions:
## 
## Variable: ER 
## H0: Coef(L0.d.ER_POS) + Coef(L1.d.ER_POS) + Coef(L2.d.ER_POS) = Coef(L0.d.ER_NEG) + Coef(L1.d.ER_NEG) + Coef(L2.d.ER_NEG) + Coef(L3.d.ER_NEG) 
## H1: Coef(L0.d.ER_POS) + Coef(L1.d.ER_POS) + Coef(L2.d.ER_POS) ≠ Coef(L0.d.ER_NEG) + Coef(L1.d.ER_NEG) + Coef(L2.d.ER_NEG) + Coef(L3.d.ER_NEG) 
## Decision: Fail to Reject H0 at 5% level. Indicating short-run symmetry for variable ER. 
## 
## Variable: PPI 
## H0: Coef(L0.d.PPI_POS) = Coef(L0.d.PPI_NEG) + Coef(L1.d.PPI_NEG) 
## H1: Coef(L0.d.PPI_POS) ≠ Coef(L0.d.PPI_NEG) + Coef(L1.d.PPI_NEG) 
## Decision: Fail to Reject H0 at 5% level. Indicating short-run symmetry for variable PPI.

Step 6: Cointegration Tests

We perform cointegration tests to assess long-term relationships using pssf(), psst(), and narayan().

PSS F Bound Test

The pssf() function tests for cointegration using the Pesaran, Shin, and Smith F Bound test.

test_result <- kardl_model |> pssf(case = 3, signif_level = "0.05")
test_result
## 
##  Pesaran-Shin-Smith (PSS) Bounds F-test for cointegration
## 
## data:  kardl_model
## F = 10.792
## alternative hypothesis: Cointegrating relationship exists

Summary of the PSS F Bound test provides detailed information about the test statistic, critical values, hypotheses, and decision regarding cointegration.

summary(test_result)
## 
## ========================================
## KARDL Cointegration Test Results
## ========================================
## 
##  Decision: Reject H0 → Cointegration (at 5% level)
## 
##  Test Statistic:
##   F: 10.7918266
## 
##  Critical Values (Lower & Upper Bounds):
##           L    U
##   10%  3.03 4.06
##   5%   3.47 4.57
##   2.5% 3.89 5.07
##   1%   4.40 5.72
## 
## 
##  Comparison:
##   At the 5% significance level, F (10.7918266) exceeds the upper bound (4.57).
##   This indicates that the variables tend to move together over  time.
##   Conclusion: There is strong evidence of a long-run relationship  (cointegration).
## 
##  Hypotheses:
## H0: Coef(L1.CPI) = Coef(L1.ER_POS) = Coef(L1.ER_NEG) = Coef(L1.PPI_POS) = Coef(L1.PPI_NEG) = 0 
## H1: Not all of Coef(L1.CPI), Coef(L1.ER_POS), Coef(L1.ER_NEG), Coef(L1.PPI_POS), Coef(L1.PPI_NEG) are zero. 
## 
##  Model Details:
##   Number of regressors (k): 4
##   Case: V 
## 
## ========================================

PSS t Bound Test

The psst() function tests the significance of the lagged dependent variable’s coefficient.

test_result <- kardl_model |> psst(case = 3, signif_level = "0.05")
test_result
## 
##  Pesaran-Shin-Smith (PSS) Bounds t-test for cointegration
## 
## data:  model
## t = -3.4418
## alternative hypothesis: Cointegrating relationship exists

Summary of the PSS t Bound test provides detailed information about the test statistic, critical values, hypotheses, and decision regarding cointegration.

summary(test_result)
## 
## ========================================
## KARDL Cointegration Test Results
## ========================================
## 
##  Decision: Inconclusive
## 
##  Test Statistic:
##   t: -3.4418095
## 
##  Critical Values (Lower & Upper Bounds):
##            L     U
##   10%  -3.13 -4.04
##   5%   -3.41 -4.36
##   2.5% -3.65 -4.62
##   1%   -3.96 -4.96
## 
## 
##  Comparison:
##   At the 5% significance level, t (3.4418095) falls between the lower bound (3.41) and upper bound (4.36).
##   This is an inconclusive zone where we cannot make a definitive  judgment.
##   Conclusion: The test does not provide clear evidence either way.
## 
##  Hypotheses:
## H0: Coef(L1.CPI) = 0 
## H1: Coef(L1.CPI) ≠ 0 
## 
##  Model Details:
##   Number of regressors (k): 4
##   Case: V 
## 
## ========================================

Narayan Test

The narayan() function is tailored for small sample sizes. It tests for cointegration using critical values optimized for small samples.

test_result <- kardl_model |> narayan(case = 3, signif_level = "0.05")
test_result
## 
##  Narayan F Test for Cointegration
## 
## data:  model
## F = 10.792
## alternative hypothesis: Cointegrating relationship exists

Summary of the Narayan test provides detailed information about the test statistic, critical values, hypotheses, and decision regarding cointegration.

summary(test_result)
## 
## ========================================
## KARDL Cointegration Test Results
## ========================================
## 
##  Decision: Reject H0 → Cointegration (at 5% level)
## 
##  Test Statistic:
##   F: 10.7918266
## 
##  Critical Values (Lower & Upper Bounds):
##           L     U
##   10% 3.160 4.230
##   5%  3.678 4.840
##   1%  4.890 6.164
## 
## 
##  Comparison:
##   At the 5% significance level, F (10.7918266) exceeds the upper bound (4.84).
##   This indicates that the variables tend to move together over  time.
##   Conclusion: There is strong evidence of a long-run relationship  (cointegration).
## 
##  Hypotheses:
## H0: Coef(L1.CPI) = Coef(L1.ER_POS) = Coef(L1.ER_NEG) = Coef(L1.PPI_POS) = Coef(L1.PPI_NEG) = 0 
## H1: Not all of Coef(L1.CPI), Coef(L1.ER_POS), Coef(L1.ER_NEG), Coef(L1.PPI_POS), Coef(L1.PPI_NEG) are zero. 
## 
##  Model Details:
##   Number of regressors (k): 4
##   Case: V 
## 
## 
##  Note:The number of observations exceeds the maximum limit for the critical valuestable. Using the critical values for 80 observations.
## ========================================

Step 7: Dynamic Multipliers

The mplier() function calculates dynamic multipliers for the model, showing how changes in independent variables affect the dependent variable over time.

multipliers <- kardl_model |> mplier()
# View multipliers of the model
head(kardl_extract(multipliers, "multipliers"))
##      h    ER_POS       ER_NEG     ER_dif    PPI_POS      PPI_NEG     PPI_dif
## [1,] 0 0.1242241  0.022936894 0.14716099 0.04200204  0.003950724  0.04595277
## [2,] 1 0.1853312 -0.003001657 0.18232956 0.05797042 -0.034155367  0.02381505
## [3,] 2 0.2203617 -0.047905918 0.17245575 0.06333773 -0.088316829 -0.02497909
## [4,] 3 0.2444547 -0.099586097 0.14486860 0.06440726 -0.147943943 -0.08353668
## [5,] 4 0.2638028 -0.153090186 0.11071257 0.06375294 -0.208741767 -0.14498883
## [6,] 5 0.2809433 -0.206422027 0.07452128 0.06242533 -0.268985893 -0.20656056
# View long-run multipliers
kardl_extract(multipliers, "omega")
## [1]  1.3801808 -0.3969313
# View short-run multipliers
head(kardl_extract(multipliers, "lambda"))
##          ER_POS      ER_NEG     PPI_POS      PPI_NEG
## [1,]  0.1242241 -0.02293689  0.04200204 -0.003950724
## [2,] -0.1103446  0.05759561 -0.04200204  0.043558804
## [3,]  0.0000000  0.00000000  0.00000000  0.000000000
## [4,]  0.0000000  0.00000000  0.00000000  0.000000000
## [5,]  0.0000000  0.00000000  0.00000000  0.000000000
## [6,]  0.0000000  0.00000000  0.00000000  0.000000000

Plotting dynamic multipliers for specific variables can be done using the plot() function, which visualizes the response of the dependent variable to changes in independent variables over time.

plot(multipliers, variables = c("ER", "PPI"))

To handle a large number of variables, you can specify a subset of variables to plot or use variables = "all" to visualize all dynamic multipliers.

Bootstrap confidence intervals for dynamic multipliers can be calculated using the bootstrap() function, which provides robust estimates of uncertainty around the multipliers.

bootstrap_results <- kardl_model |>
  bootstrap(horizon = 12, replications = 10)
# View bootstrap summary
summary(bootstrap_results)
## Summary of Dynamic Multipliers
## Horizon: 12 
## 
##        h          ER_POS           ER_NEG             ER_dif        
##  Min.   : 0   Min.   :0.1242   Min.   :-0.54519   Min.   :-0.16380  
##  1st Qu.: 3   1st Qu.:0.2445   1st Qu.:-0.40804   1st Qu.:-0.06693  
##  Median : 6   Median :0.2969   Median :-0.25879   Median : 0.03813  
##  Mean   : 6   Mean   :0.2847   Mean   :-0.25573   Mean   : 0.02895  
##  3rd Qu.: 9   3rd Qu.:0.3411   3rd Qu.:-0.09959   3rd Qu.: 0.14487  
##  Max.   :12   Max.   :0.3814   Max.   : 0.02294   Max.   : 0.18233  
##     PPI_POS           PPI_NEG             PPI_dif          ER_CI_upper      
##  Min.   :0.04200   Min.   :-0.650249   Min.   :-0.59891   Min.   :-0.03333  
##  1st Qu.:0.05437   1st Qu.:-0.495965   1st Qu.:-0.44002   1st Qu.: 0.05680  
##  Median :0.05797   Median :-0.328001   Median :-0.26715   Median : 0.15484  
##  Mean   :0.05739   Mean   :-0.322665   Mean   :-0.26528   Mean   : 0.13944  
##  3rd Qu.:0.06243   3rd Qu.:-0.147944   3rd Qu.:-0.08354   3rd Qu.: 0.22095  
##  Max.   :0.06441   Max.   : 0.003951   Max.   : 0.04595   Max.   : 0.26922  
##   ER_CI_lower        PPI_CI_upper       PPI_CI_lower     
##  Min.   :-0.55120   Min.   :-0.53877   Min.   :-0.79620  
##  1st Qu.:-0.40167   1st Qu.:-0.38399   1st Qu.:-0.60886  
##  Median :-0.23798   Median :-0.21619   Median :-0.39904  
##  Mean   :-0.23136   Mean   :-0.20743   Mean   :-0.39613  
##  3rd Qu.:-0.06004   3rd Qu.:-0.01338   3rd Qu.:-0.19664  
##  Max.   : 0.08743   Max.   : 0.08994   Max.   : 0.01631

Visualize bootstrap results for specific variables to understand the variability and confidence intervals of the dynamic multipliers.

plot(bootstrap_results, variables = "ER")

Step 8: Customizing asymmetric Variables

We demonstrate how to customize prefixes and suffixes for asymmetric variables using kardl_set().

# Set custom prefixes and suffixes
kardl_reset()
kardl_set(asym_prefix = c("asyP_", "asyN_"), asym_suffix = c("_PP", "_NN"))
kardl_custom <- kardl(data = imf_example_data, my_formula)
kardl_custom
## Optimal lags for each variable ( AIC ):
## CPI: 2, asyP_ER_PP: 1, asyN_ER_NN: 0, asyP_PPI_PP: 4, asyN_PPI_NN: 0 
## 
## Call:
## lm(formula = my_formula, data = model_data)
## 
## Coefficients:
##      (Intercept)            L1.CPI     L1.asyP_ER_PP     L1.asyN_ER_NN  
##       -0.0404958        -0.0128695         0.0120635         0.0258122  
##   L1.asyP_PPI_PP    L1.asyN_PPI_NN          L1.d.CPI          L2.d.CPI  
##        0.0511977         0.0433195         0.3830822        -0.0920420  
##  L0.d.asyP_ER_PP   L1.d.asyP_ER_PP   L0.d.asyN_ER_NN  L0.d.asyP_PPI_PP  
##        0.1122301         0.0880910        -0.0017889         0.0509193  
## L1.d.asyP_PPI_PP  L2.d.asyP_PPI_PP  L3.d.asyP_PPI_PP  L4.d.asyP_PPI_PP  
##        0.0003349        -0.0535510        -0.0435529         0.0118365  
## L0.d.asyN_PPI_NN             covid             trend  
##       -0.0011170         0.0026926        -0.0003479

Key Functions and Parameters

For detailed documentation, use ?kardl, ?kardl_set, ?kardl_longrun, ?symmetrytest, ?pssf, ?psst, ?narayan, or ?ecm.

Conclusion

The kardl package is a versatile tool for econometric analysis, offering robust support for symmetric and asymmetric ARDL/NARDL modeling, cointegration tests, stability diagnostics, and heteroskedasticity checks. Its flexible formula specification, lag optimization, and support for parallel processing make it ideal for studying complex economic relationships. For more information, visit https://github.com/karamelikli/kardl or contact the authors at hakperest@gmail.com.